Audio Transcription & Intent Labeling for Voice Assistant AI
I contributed to a voice AI training project focused on developing a smart assistant capable of understanding natural human speech across diverse accents and contexts. My role involved transcribing short audio clips with high accuracy and labeling each segment based on speaker intent, emotion, background noise, and language usage. This required a deep understanding of conversational nuances and strong attention to detail to ensure the final dataset met machine learning model standards. Working through platforms such as Appen and TELUS International, I adhered to strict guidelines for timestamp alignment, speaker identification, and context tagging. I also performed validation tasks, reviewing and correcting automated transcriptions to increase dataset reliability. This project played a crucial role in enhancing the speech recognition and contextual understanding capabilities of the client’s virtual assistant product, improving performance in real-world user interactions.